/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

#include <sstream>

#include <gtest/gtest.h>
#include "ModelConfig.pb.h"
#include "paddle/gserver/layers/DataLayer.h"
#include "paddle/trainer/Trainer.h"

#include "LayerGradUtil.h"
#include "paddle/testing/TestUtil.h"

using namespace paddle;  // NOLINT

DECLARE_int32(gpu_id);
DECLARE_bool(thread_local_rand_use_global_seed);

struct SingleBeamExpansion {
  vector<int> seqStartPos;
  vector<int> subSeqStartPos;

  vector<real> candidateScores;
  // TODO(caoying): store this into Argument.ids
  vector<real> selectedIndices;
  vector<int> groundTruth;
};

void genRandomBeamExpansion(size_t expansionCount,
                            vector<SingleBeamExpansion>& beamExpansions) {
  beamExpansions.clear();
}

void testCrossEntropyOverBeam() {
  const size_t expansionCount = 3;
  vector<SingleBeamExpansion> beams;
  genRandomBeamExpansion(expansionCount, beams);

  for (size_t i = 0; i < beams.size(); ++i) {
    const SingleBeamExpansion& beam = beams[i];
    // create scores for all the candidates
    MatrixPtr candidateScorePtr =
        Matrix::create(beam.candidateScores.size(), 1, false, false);
    candidateScorePtr->copyFrom(candidateScores.data(), candidateScores.size());

    ostringstream paramName;
    paramName << "candidate_scores_" << i;
    beam.subSeqStartPos.size()
        ? config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA,
                                      ostr.str(),
                                      candidateScorePtr,
                                      beam.seqStartPos,
                                      beam.subSeqStartPos})
        : config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA,
                                      ostr.str(),
                                      candidateScorePtr,
                                      beam.seqStartPos});
    // create indices for the selected candidates

    // create the ground truth
  }
}

TestConfig config;
config.layerConfig.set_type("cross_entropy_over_beam");

// testLayerGrad(
//     config, "cross_entropy_over_beam", seqNum, false, useGpu, false);
}

TEST(Layer, CrossEntropyOverBeam) {
  for (bool useGpu : {false, true}) testCrossEntropyOverBeam(useGpu);
}

int main(int argc, char** argv) {
  initMain(argc, argv);
  hl_start();
  hl_init(FLAGS_gpu_id);
  FLAGS_thread_local_rand_use_global_seed = true;
  srand(1);
  testing::InitGoogleTest(&argc, argv);
  return RUN_ALL_TESTS();
}